Project Summary The overarching goal of this application is to advance the field of population-based research in breast cancer disparities through innovative statistical techniques. This objective of this study is to address racial and geospatial disparities in triple-negative breast cancer diagnosis and survival and examine potential predictors of both diagnosis and survival. Breast cancer, in general, carries an enormous public health burden and triple- negative breast cancer is accounts for 15% of all breast cancer diagnoses. Morbidity and mortality burdens are higher with this type of breast cancer and diagnosis has been significantly associated with younger age, African American race, later stage diagnosis, lower socioeconomic status and shortened survival. The proposed study will be the first of its kind to use data from the United States Cancer Statistics database which includes combined cancer incidence data from the Center for Disease Control and Prevention?s (CDC) National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology, and End Results (SEER) Program covering 99% of the population in comparison to 28% with SEER data alone. This study will focus on racial disparities between African-American and White women and geospatial disparities across the contiguous United States. It will evaluate individual, social and physical environmental factors that contribute to disparate rates of diagnosis and survival. Predictors of interest include, but are not limited to, person level-predictors ? race, age, and stage of diagnosis, county-level predictors ? residential segregation, social capital and socioeconomic climate, and state-level predictors ? breast cancer screening mandates, and implementation of Medicare/Medicaid expansion and state-specific restrictions on Nurse Practitioner or Physician Assistant scope of practice. Descriptive epidemiologic analysis will allow us to compare incidence of triple negative breast cancer across race and age groups at multiple geographic levels. Exploratory spatial data analysis will be used to create descriptive maps and evaluate patterns of geospatial clustering and underlying community characteristics. Multilevel modeling with latent variables will allow us to explore predictors of triple-negative breast cancer diagnosis and survival. Results will robustly answer the question of both ?why?? and ?where?? thus potentially informing policy at actionable geographic levels and adding valuable information to cancer health disparities research as a whole.